A Single-Pass Algorithm for Spectrum Estimation With Fast Convergence

Xiao H.; Wu W. B.

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A Single-Pass Algorithm for Spectrum Estimation With Fast Convergence

机译：具有快速收敛性的频谱估计的单遍算法

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摘要

We propose a single-pass algorithm for estimating spectral densities of stationary processes. Our algorithm is computationally fast in the sense that, when a new observation arrives, it can provide a real-time update within $O(1)$ computation. The proposed algorithm is probabilistically fast in that, for stationary processes whose auto-covariances decay geometrically, the estimates from the algorithm converge at a rate which is optimal up to a multiplicative logarithmic factor. We also establish asymptotic normality for the recursive estimate. A simulation study is carried out and it confirms the superiority over the classical batched mean estimates.

机译：我们提出了一种用于估算固定过程光谱密度的单程算法。从某种意义上说，我们的算法在计算上是快速的，当新的观测值到达时，它可以在 $ O（1）$ 计算。所提出的算法在概率上是快速的，因为对于自协方差在几何上衰减的平稳过程，该算法的估计值以最高达一个对数对数因子的最优速率收敛。我们还为递归估计建立渐近正态性。进行了仿真研究，它证实了优于经典的批处理均值估计的优势。

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来源
《Information Theory, IEEE Transactions on》 |2011年第7期|p.4720-4731|共12页
作者
Xiao H.; Wu W. B.;
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作者单位

Department of Statistics, The University of Chicago, Chicago, IL, USA;

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原文格式 PDF
正文语种 eng
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关键词
Batched mean estimate; bias reduction; nonparametric estimation; physical dependence measure; recursive algorithm; spectral density; stochastic process;

机译：批量均值估计;偏倚减少;非参数估计;物理相关性测度;递归算法;谱密度;随机过程;

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A Single-Pass Algorithm for Spectrum Estimation With Fast Convergence

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